A Wireless Implantable Passive Strain Sensor System

Umbrecht, F.; Wendlandt, Michael; Juncker, David; Hierold, Christofer; Neuenschwander, J.

doi:10.1109/icsens.2005.1597627

Cited by 18 publications

(13 citation statements)

References 24 publications

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“…Certain sensing environments benefit from, if not require, a wireless sensor embodiment. For instance, wireless strain sensors have been demonstrated for medical applications to monitor human biological factors, thus avoiding invasive wired techniques [1]- [3]. Interest has also been expressed for structural health monitoring (SHM) applications where wired strain sensors cannot be used due to moving parts or where wire weight and routing are a concern [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Wireless SAW Strain Sensor Using Orthogonal Frequency Coding

Humphries

Malocha

2015

IEEE Sensors J.

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Wireless sensors offer a unique solution for many applications where wired designs may not be feasible. This paper presents the design and experimental results of a passive, wireless surface acoustic wave (SAW) strain sensor using orthogonal frequency coding (OFC) for the device identification. The sensor operates at a frequency of 915 MHz with a bandwidth of 68 MHz. A vector network analyzer is used to interrogate the sensor from a range of 60 cm (2 ft), with further range possible by increased transmitter output power or averaging. The sensor is bonded directly to a steel cantilever test structure and could be attached directly to other structures. An external connection site facilitates an antenna, with bond wires joining the connection site to the SAW die. A 3-D printed package protects the sensor while allowing the antenna to be mounted externally from the sensor package. Experimental results show that the wireless SAW strain sensor performs comparably to a wired, commercial strain gage. In addition, the strain sensitivity is extracted for this SAW OFC strain sensor embodiment which describes the frequency shift with the applied strain (−1.80 ppm/με).Index Terms-Wireless sensor, saw sensor, surface acoustic wave (saw), structural health monitoring (shm), saw strain sensor.

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Section: Introductionmentioning

confidence: 99%

Wireless SAW Strain Sensor Using Orthogonal Frequency Coding

Humphries

Malocha

2015

IEEE Sensors J.

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“…A large body of research has been demonstrated on various biomedical applications of sensors, but most efforts have focused on clinical monitoring rather than TE/RM or preclinical applications [80,81]. Implantable sensors differ significantly in their designs and fabrication techniques, but the endpoint sensing modalities include biopotential [82], electrical impedance [83], pressure [84,85], flow [86,87], strain [88], oxygen [89], pH [83], and glucose [90,91].…”

Section: Implantable Sensors Applications In Te/rmmentioning

confidence: 99%

Implantable Sensors for Regenerative Medicine

Klosterhoff

Tsang

She

et al. 2017

Journal of Biomechanical Engineering

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The translation of many tissue engineering/regenerative medicine (TE/RM) therapies that demonstrate promise in vitro are delayed or abandoned due to reduced and inconsistent efficacy when implemented in more complex and clinically relevant preclinical in vivo models. Determining mechanistic reasons for impaired treatment efficacy is challenging after a regenerative therapy is implanted due to technical limitations in longitudinally measuring the progression of key environmental cues in vivo. The ability to acquire real-time measurements of environmental parameters of interest including strain, pressure, pH, temperature, oxygen tension, and specific biomarkers within the regenerative niche in situ would significantly enhance the information available to tissue engineers to monitor and evaluate mechanisms of functional healing or lack thereof. Continued advancements in material and fabrication technologies utilized by microelectromechanical systems (MEMSs) and the unique physical characteristics of passive magnetoelastic sensor platforms have created an opportunity to implant small, flexible, low-power sensors into preclinical in vivo models, and quantitatively measure environmental cues throughout healing. In this perspective article, we discuss the need for longitudinal measurements in TE/RM research, technical progress in MEMS and magnetoelastic approaches to implantable sensors, the potential application of implantable sensors to benefit preclinical TE/RM research, and the future directions of collaborative efforts at the intersection of these two important fields.

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“…Assuming that k is upper bounded by N , which is the case for finite capacity channels, then Equation (9) represents N equations in P k f , β, and together with the constraint in Equation (10) (9) on Equation (3) and simplifying 1 , we get:…”

Section: Optimal Detector Designmentioning

confidence: 99%

“…An example of a passive wireless sensor for measuring temperature, stress, strain, acceleration and displacement using Surface Acoustic Wave (SAW) transducer is described in [8]. In the health care domain, a passive strainsensor technology for the measurement of small strains on bones or fixation systems in the human body is presented in [9]. In the automotive industry, passive wireless strain monitoring of car tires is described in [10].…”

Section: Introductionmentioning

confidence: 99%

Optimal performance for detection systems in wireless passive sensor networks

Tantawy

Koutsoukos

Biswas

2009

2009 17th Mediterranean Conference on Control and Automation

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Abstract-Passive wireless sensors have emerged as a new technology to measure a vast majority of phenomena in our daily life. Passive sensors require no power source, and therefore their application domains are numerous, including health care, infrastructure protection, and national security, among many others. The deployment of wireless passive sensors and their readers has changed how detection needs to be performed. Passive sensors cannot pre-process the measurements as they have limited computational power. Therefore, no local decision is taken. Also, the reader polls the information from multiple sensors at the same time, and this causes collisions and hence packet drops and delays. In this paper, we formulate the detection performance, with non-ideal channels, in a probabilistic way, and compare with classical detection performance. We design an optimal adaptive Neyman-Pearson detector, given the channel probabilistic model, by formulating and solving a constrained optimization problem.

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A Wireless Implantable Passive Strain Sensor System

Cited by 18 publications

References 24 publications

Wireless SAW Strain Sensor Using Orthogonal Frequency Coding

Wireless SAW Strain Sensor Using Orthogonal Frequency Coding

Implantable Sensors for Regenerative Medicine

Optimal performance for detection systems in wireless passive sensor networks

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